In the Transformer architecture, the weight matrices used to generate the Query (Q), Key (K), and Value (V) vectors do not change with each individual input value or token during inference. These weight matrices are learned parameters of the model, optimized during the training phase through back-propagation. Once training is complete, they remain fixed during the forward pass (inference) for all inputs.